Overview

Brought to you by YData

Dataset statistics

Number of variables19
Number of observations118917
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory17.2 MiB
Average record size in memory152.0 B

Variable types

Text6
Numeric9
Categorical2
DateTime2

Alerts

che_pc_usd is highly overall correlated with che_perc_gdp and 1 other fieldsHigh correlation
che_perc_gdp is highly overall correlated with che_pc_usd and 1 other fieldsHigh correlation
country is highly overall correlated with che_pc_usd and 4 other fieldsHigh correlation
insurance_perc_che is highly overall correlated with countryHigh correlation
population is highly overall correlated with countryHigh correlation
prev_perc is highly overall correlated with price_unitHigh correlation
price_month is highly overall correlated with price_unitHigh correlation
price_unit is highly overall correlated with prev_perc and 1 other fieldsHigh correlation
public_perc_che is highly overall correlated with countryHigh correlation
price_unit is highly skewed (γ1 = 87.52046958) Skewed

Reproduction

Analysis started2024-11-29 02:50:07.344272
Analysis finished2024-11-29 02:50:17.806013
Duration10.46 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

brand
Text

Distinct591
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size929.2 KiB
2024-11-29T03:50:17.988377image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters1189170
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique34 ?
Unique (%)< 0.1%

Sample

1st rowBRAND_354E
2nd rowBRAND_626D
3rd rowBRAND_45D9
4th rowBRAND_D724
5th rowBRAND_4887
ValueCountFrequency (%)
brand_0056 2366
 
2.0%
brand_62c7 1875
 
1.6%
brand_7a2e 1762
 
1.5%
brand_a12a 1761
 
1.5%
brand_4048 1642
 
1.4%
brand_076f 1521
 
1.3%
brand_cfd9 1449
 
1.2%
brand_71fa 1435
 
1.2%
brand_fcb2 1369
 
1.2%
brand_d724 1305
 
1.1%
Other values (581) 102432
86.1%
2024-11-29T03:50:18.296789image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 152540
12.8%
B 151293
12.7%
D 144448
12.1%
N 118917
10.0%
_ 118917
10.0%
R 118917
10.0%
6 36146
 
3.0%
2 34638
 
2.9%
0 33599
 
2.8%
9 31823
 
2.7%
Other values (9) 247932
20.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1189170
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 152540
12.8%
B 151293
12.7%
D 144448
12.1%
N 118917
10.0%
_ 118917
10.0%
R 118917
10.0%
6 36146
 
3.0%
2 34638
 
2.9%
0 33599
 
2.8%
9 31823
 
2.7%
Other values (9) 247932
20.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1189170
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 152540
12.8%
B 151293
12.7%
D 144448
12.1%
N 118917
10.0%
_ 118917
10.0%
R 118917
10.0%
6 36146
 
3.0%
2 34638
 
2.9%
0 33599
 
2.8%
9 31823
 
2.7%
Other values (9) 247932
20.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1189170
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 152540
12.8%
B 151293
12.7%
D 144448
12.1%
N 118917
10.0%
_ 118917
10.0%
R 118917
10.0%
6 36146
 
3.0%
2 34638
 
2.9%
0 33599
 
2.8%
9 31823
 
2.7%
Other values (9) 247932
20.8%

che_pc_usd
Real number (ℝ)

High correlation 

Distinct396
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5293892
Minimum-1
Maximum2.6569132
Zeros0
Zeros (%)0.0%
Negative741
Negative (%)0.6%
Memory size929.2 KiB
2024-11-29T03:50:18.444773image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile1.05696
Q11.1799313
median1.4723783
Q31.8164794
95-th percentile2.2862047
Maximum2.6569132
Range3.6569132
Interquartile range (IQR)0.63654806

Descriptive statistics

Standard deviation0.43909375
Coefficient of variation (CV)0.28710399
Kurtosis5.6968711
Mean1.5293892
Median Absolute Deviation (MAD)0.31148564
Skewness-0.6786595
Sum181870.38
Variance0.19280332
MonotonicityNot monotonic
2024-11-29T03:50:18.574818image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.680087391 1568
 
1.3%
1.05696005 1172
 
1.0%
1.104712859 1070
 
0.9%
1.418695381 1006
 
0.8%
1.448033708 961
 
0.8%
1.423064919 957
 
0.8%
1.07943196 929
 
0.8%
1.678682896 881
 
0.7%
2.503901373 871
 
0.7%
1.660892634 855
 
0.7%
Other values (386) 108647
91.4%
ValueCountFrequency (%)
-1 741
0.6%
1 10
 
< 0.1%
1.00062422 12
 
< 0.1%
1.001560549 12
 
< 0.1%
1.001872659 18
 
< 0.1%
1.002340824 24
 
< 0.1%
1.002808989 14
 
< 0.1%
1.003277154 12
 
< 0.1%
1.003755484 12
 
< 0.1%
1.004057428 1
 
< 0.1%
ValueCountFrequency (%)
2.656913233 676
0.6%
2.604244694 804
0.7%
2.535736579 850
0.7%
2.503901373 871
0.7%
2.494382022 690
0.6%
2.490168539 175
 
0.1%
2.468320849 548
0.5%
2.459581773 368
0.3%
2.442883895 20
 
< 0.1%
2.437421973 11
 
< 0.1%

che_perc_gdp
Real number (ℝ)

High correlation 

Distinct409
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6070998
Minimum-1
Maximum2.3111028
Zeros0
Zeros (%)0.0%
Negative3644
Negative (%)3.1%
Memory size929.2 KiB
2024-11-29T03:50:18.695851image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile1.0731578
Q11.4648487
median1.731474
Q31.8941392
95-th percentile2.0517702
Maximum2.3111028
Range3.3111028
Interquartile range (IQR)0.42929047

Descriptive statistics

Standard deviation0.53799405
Coefficient of variation (CV)0.33476082
Kurtosis14.067911
Mean1.6070998
Median Absolute Deviation (MAD)0.19446208
Skewness-3.4326905
Sum191111.49
Variance0.28943759
MonotonicityNot monotonic
2024-11-29T03:50:18.817372image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1 3644
 
3.1%
1.730520606 1006
 
0.8%
1.941136092 961
 
0.8%
1.753337696 957
 
0.8%
1.977700795 881
 
0.7%
1.877291415 871
 
0.7%
2.035271828 863
 
0.7%
1.659159354 855
 
0.7%
2.054177364 851
 
0.7%
2.037798443 851
 
0.7%
Other values (399) 107177
90.1%
ValueCountFrequency (%)
-1 3644
3.1%
1 10
 
< 0.1%
1.020151729 12
 
< 0.1%
1.036612732 12
 
< 0.1%
1.04042123 533
 
0.4%
1.043470525 104
 
0.1%
1.044247892 24
 
< 0.1%
1.046249573 59
 
< 0.1%
1.051142034 18
 
< 0.1%
1.051941331 12
 
< 0.1%
ValueCountFrequency (%)
2.311102801 617
0.5%
2.208068896 787
0.7%
2.206787733 501
0.4%
2.160170084 553
0.5%
2.146527555 14
 
< 0.1%
2.126096163 263
 
0.2%
2.103845172 96
 
0.1%
2.102405175 731
0.6%
2.085983813 118
 
0.1%
2.085917456 99
 
0.1%

cluster_nl
Text

Distinct2716
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size929.2 KiB
2024-11-29T03:50:18.980404image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length23
Median length23
Mean length23
Min length23

Characters and Unicode

Total characters2735091
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique63 ?
Unique (%)0.1%

Sample

1st rowBRAND_354E_COUNTRY_88A3
2nd rowBRAND_626D_COUNTRY_8B47
3rd rowBRAND_45D9_COUNTRY_88A3
4th rowBRAND_D724_COUNTRY_445D
5th rowBRAND_4887_COUNTRY_D8B0
ValueCountFrequency (%)
brand_354e_country_88a3 60
 
0.1%
brand_3ba7_country_445d 60
 
0.1%
brand_c21a_country_4442 60
 
0.1%
brand_7a2e_country_d5b9 60
 
0.1%
brand_f886_country_d5b9 60
 
0.1%
brand_88b9_country_53a5 60
 
0.1%
brand_ccaa_country_c8f4 60
 
0.1%
brand_e551_country_8dbb 60
 
0.1%
brand_061c_country_6f78 60
 
0.1%
brand_061c_country_1007 60
 
0.1%
Other values (2706) 118317
99.5%
2024-11-29T03:50:19.223974image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
_ 356751
13.0%
N 237834
 
8.7%
R 237834
 
8.7%
A 178401
 
6.5%
B 178143
 
6.5%
D 172986
 
6.3%
C 163356
 
6.0%
Y 118917
 
4.3%
T 118917
 
4.3%
U 118917
 
4.3%
Other values (13) 853035
31.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2735091
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
_ 356751
13.0%
N 237834
 
8.7%
R 237834
 
8.7%
A 178401
 
6.5%
B 178143
 
6.5%
D 172986
 
6.3%
C 163356
 
6.0%
Y 118917
 
4.3%
T 118917
 
4.3%
U 118917
 
4.3%
Other values (13) 853035
31.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2735091
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
_ 356751
13.0%
N 237834
 
8.7%
R 237834
 
8.7%
A 178401
 
6.5%
B 178143
 
6.5%
D 172986
 
6.3%
C 163356
 
6.0%
Y 118917
 
4.3%
T 118917
 
4.3%
U 118917
 
4.3%
Other values (13) 853035
31.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2735091
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
_ 356751
13.0%
N 237834
 
8.7%
R 237834
 
8.7%
A 178401
 
6.5%
B 178143
 
6.5%
D 172986
 
6.3%
C 163356
 
6.0%
Y 118917
 
4.3%
T 118917
 
4.3%
U 118917
 
4.3%
Other values (13) 853035
31.2%

corporation
Text

Distinct136
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size929.2 KiB
2024-11-29T03:50:19.438006image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters1070253
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowCORP_D524
2nd rowCORP_01C7
3rd rowCORP_39F7
4th rowCORP_711A
5th rowCORP_443D
ValueCountFrequency (%)
corp_01c7 19445
16.4%
corp_5cbd 9004
 
7.6%
corp_c868 7771
 
6.5%
corp_8f4f 6502
 
5.5%
corp_a713 5879
 
4.9%
corp_443d 5309
 
4.5%
corp_39f7 5149
 
4.3%
corp_a682 5070
 
4.3%
corp_09bb 4726
 
4.0%
corp_a278 4035
 
3.4%
Other values (126) 46027
38.7%
2024-11-29T03:50:19.744103image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 162413
15.2%
R 118917
11.1%
P 118917
11.1%
_ 118917
11.1%
O 118917
11.1%
7 52531
 
4.9%
8 44659
 
4.2%
1 39553
 
3.7%
0 34920
 
3.3%
B 30737
 
2.9%
Other values (10) 229772
21.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1070253
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 162413
15.2%
R 118917
11.1%
P 118917
11.1%
_ 118917
11.1%
O 118917
11.1%
7 52531
 
4.9%
8 44659
 
4.2%
1 39553
 
3.7%
0 34920
 
3.3%
B 30737
 
2.9%
Other values (10) 229772
21.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1070253
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 162413
15.2%
R 118917
11.1%
P 118917
11.1%
_ 118917
11.1%
O 118917
11.1%
7 52531
 
4.9%
8 44659
 
4.2%
1 39553
 
3.7%
0 34920
 
3.3%
B 30737
 
2.9%
Other values (10) 229772
21.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1070253
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 162413
15.2%
R 118917
11.1%
P 118917
11.1%
_ 118917
11.1%
O 118917
11.1%
7 52531
 
4.9%
8 44659
 
4.2%
1 39553
 
3.7%
0 34920
 
3.3%
B 30737
 
2.9%
Other values (10) 229772
21.5%

country
Categorical

High correlation 

Distinct49
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size929.2 KiB
COUNTRY_907E
 
5522
COUNTRY_3AD0
 
5002
COUNTRY_89F9
 
4811
COUNTRY_53A5
 
4782
COUNTRY_D8B0
 
4654
Other values (44)
94146 

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters1427004
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCOUNTRY_88A3
2nd rowCOUNTRY_8B47
3rd rowCOUNTRY_88A3
4th rowCOUNTRY_445D
5th rowCOUNTRY_D8B0

Common Values

ValueCountFrequency (%)
COUNTRY_907E 5522
 
4.6%
COUNTRY_3AD0 5002
 
4.2%
COUNTRY_89F9 4811
 
4.0%
COUNTRY_53A5 4782
 
4.0%
COUNTRY_D8B0 4654
 
3.9%
COUNTRY_445D 4547
 
3.8%
COUNTRY_4242 4425
 
3.7%
COUNTRY_1007 4119
 
3.5%
COUNTRY_9891 3891
 
3.3%
COUNTRY_6F78 3810
 
3.2%
Other values (39) 73354
61.7%

Length

2024-11-29T03:50:19.853632image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
country_907e 5522
 
4.6%
country_3ad0 5002
 
4.2%
country_89f9 4811
 
4.0%
country_53a5 4782
 
4.0%
country_d8b0 4654
 
3.9%
country_445d 4547
 
3.8%
country_4242 4425
 
3.7%
country_1007 4119
 
3.5%
country_9891 3891
 
3.3%
country_6f78 3810
 
3.2%
Other values (39) 73354
61.7%

Most occurring characters

ValueCountFrequency (%)
C 132674
 
9.3%
O 118917
 
8.3%
U 118917
 
8.3%
N 118917
 
8.3%
T 118917
 
8.3%
R 118917
 
8.3%
Y 118917
 
8.3%
_ 118917
 
8.3%
4 48840
 
3.4%
8 44576
 
3.1%
Other values (13) 368495
25.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1427004
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 132674
 
9.3%
O 118917
 
8.3%
U 118917
 
8.3%
N 118917
 
8.3%
T 118917
 
8.3%
R 118917
 
8.3%
Y 118917
 
8.3%
_ 118917
 
8.3%
4 48840
 
3.4%
8 44576
 
3.1%
Other values (13) 368495
25.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1427004
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 132674
 
9.3%
O 118917
 
8.3%
U 118917
 
8.3%
N 118917
 
8.3%
T 118917
 
8.3%
R 118917
 
8.3%
Y 118917
 
8.3%
_ 118917
 
8.3%
4 48840
 
3.4%
8 44576
 
3.1%
Other values (13) 368495
25.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1427004
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 132674
 
9.3%
O 118917
 
8.3%
U 118917
 
8.3%
N 118917
 
8.3%
T 118917
 
8.3%
R 118917
 
8.3%
Y 118917
 
8.3%
_ 118917
 
8.3%
4 48840
 
3.4%
8 44576
 
3.1%
Other values (13) 368495
25.8%

launch_date
Date

Distinct103
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size929.2 KiB
Minimum2014-06-01 00:00:00
Maximum2022-12-01 00:00:00
2024-11-29T03:50:19.958157image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T03:50:20.200321image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

date
Date

Distinct103
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size929.2 KiB
Minimum2014-06-01 00:00:00
Maximum2022-12-01 00:00:00
2024-11-29T03:50:20.323658image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T03:50:20.458212image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

drug_id
Text

Distinct257
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size929.2 KiB
2024-11-29T03:50:20.685770image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters1427004
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st rowDRUG_ID_8795
2nd rowDRUG_ID_E66E
3rd rowDRUG_ID_F272
4th rowDRUG_ID_1D4E
5th rowDRUG_ID_AA88
ValueCountFrequency (%)
drug_id_3a6f 3035
 
2.6%
drug_id_d637 2567
 
2.2%
drug_id_b15f 1852
 
1.6%
drug_id_30f8 1841
 
1.5%
drug_id_b633 1828
 
1.5%
drug_id_3416 1702
 
1.4%
drug_id_be46 1676
 
1.4%
drug_id_eee7 1592
 
1.3%
drug_id_473b 1544
 
1.3%
drug_id_b8a8 1521
 
1.3%
Other values (247) 99759
83.9%
2024-11-29T03:50:20.980325image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
D 267269
18.7%
_ 237834
16.7%
R 118917
8.3%
U 118917
8.3%
G 118917
8.3%
I 118917
8.3%
7 44979
 
3.2%
3 41096
 
2.9%
8 35879
 
2.5%
B 33678
 
2.4%
Other values (11) 290601
20.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1427004
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
D 267269
18.7%
_ 237834
16.7%
R 118917
8.3%
U 118917
8.3%
G 118917
8.3%
I 118917
8.3%
7 44979
 
3.2%
3 41096
 
2.9%
8 35879
 
2.5%
B 33678
 
2.4%
Other values (11) 290601
20.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1427004
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
D 267269
18.7%
_ 237834
16.7%
R 118917
8.3%
U 118917
8.3%
G 118917
8.3%
I 118917
8.3%
7 44979
 
3.2%
3 41096
 
2.9%
8 35879
 
2.5%
B 33678
 
2.4%
Other values (11) 290601
20.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1427004
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
D 267269
18.7%
_ 237834
16.7%
R 118917
8.3%
U 118917
8.3%
G 118917
8.3%
I 118917
8.3%
7 44979
 
3.2%
3 41096
 
2.9%
8 35879
 
2.5%
B 33678
 
2.4%
Other values (11) 290601
20.4%

ind_launch_date
Text

Distinct123
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size929.2 KiB
2024-11-29T03:50:21.161855image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length19
Median length2
Mean length6.4488172
Min length2

Characters and Unicode

Total characters766874
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st row-1
2nd row2014-09-01 00:00:00
3rd row-1
4th row-1
5th row-1
ValueCountFrequency (%)
1 87797
58.5%
00:00:00 31120
 
20.7%
2019-11-01 2234
 
1.5%
2020-03-01 1904
 
1.3%
2019-08-01 1465
 
1.0%
2020-01-01 1154
 
0.8%
2019-07-01 967
 
0.6%
2020-08-01 876
 
0.6%
2020-02-01 860
 
0.6%
2020-11-01 816
 
0.5%
Other values (114) 20844
 
13.9%
2024-11-29T03:50:21.446402image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 280359
36.6%
1 159617
20.8%
- 150037
19.6%
: 62240
 
8.1%
2 50583
 
6.6%
31120
 
4.1%
9 9159
 
1.2%
7 5597
 
0.7%
8 5443
 
0.7%
3 4295
 
0.6%
Other values (3) 8424
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 766874
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 280359
36.6%
1 159617
20.8%
- 150037
19.6%
: 62240
 
8.1%
2 50583
 
6.6%
31120
 
4.1%
9 9159
 
1.2%
7 5597
 
0.7%
8 5443
 
0.7%
3 4295
 
0.6%
Other values (3) 8424
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 766874
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 280359
36.6%
1 159617
20.8%
- 150037
19.6%
: 62240
 
8.1%
2 50583
 
6.6%
31120
 
4.1%
9 9159
 
1.2%
7 5597
 
0.7%
8 5443
 
0.7%
3 4295
 
0.6%
Other values (3) 8424
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 766874
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 280359
36.6%
1 159617
20.8%
- 150037
19.6%
: 62240
 
8.1%
2 50583
 
6.6%
31120
 
4.1%
9 9159
 
1.2%
7 5597
 
0.7%
8 5443
 
0.7%
3 4295
 
0.6%
Other values (3) 8424
 
1.1%

indication
Text

Distinct257
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size929.2 KiB
2024-11-29T03:50:21.667741image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length168
Median length12
Mean length18.811373
Min length12

Characters and Unicode

Total characters2236992
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row['IND_C3B6']
2nd row['IND_1590', 'IND_ECAC']
3rd row['IND_B2EF']
4th row['IND_BAFB']
5th row['IND_3F31']
ValueCountFrequency (%)
ind_3a0d 20621
 
11.1%
ind_617c 12986
 
7.0%
ind_b2ef 11227
 
6.0%
ind_f338 7303
 
3.9%
ind_da0b 7185
 
3.9%
ind_c3b6 6814
 
3.7%
ind_7c11 5678
 
3.0%
ind_bafb 5611
 
3.0%
ind_bd8b 5345
 
2.9%
ind_c829 4614
 
2.5%
Other values (145) 99032
53.1%
2024-11-29T03:50:21.994051image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 372832
16.7%
D 241283
 
10.8%
I 186416
 
8.3%
N 186416
 
8.3%
_ 186416
 
8.3%
[ 118917
 
5.3%
] 118917
 
5.3%
, 67499
 
3.0%
67499
 
3.0%
A 61225
 
2.7%
Other values (14) 629572
28.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2236992
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
' 372832
16.7%
D 241283
 
10.8%
I 186416
 
8.3%
N 186416
 
8.3%
_ 186416
 
8.3%
[ 118917
 
5.3%
] 118917
 
5.3%
, 67499
 
3.0%
67499
 
3.0%
A 61225
 
2.7%
Other values (14) 629572
28.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2236992
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
' 372832
16.7%
D 241283
 
10.8%
I 186416
 
8.3%
N 186416
 
8.3%
_ 186416
 
8.3%
[ 118917
 
5.3%
] 118917
 
5.3%
, 67499
 
3.0%
67499
 
3.0%
A 61225
 
2.7%
Other values (14) 629572
28.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2236992
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
' 372832
16.7%
D 241283
 
10.8%
I 186416
 
8.3%
N 186416
 
8.3%
_ 186416
 
8.3%
[ 118917
 
5.3%
] 118917
 
5.3%
, 67499
 
3.0%
67499
 
3.0%
A 61225
 
2.7%
Other values (14) 629572
28.1%

insurance_perc_che
Real number (ℝ)

High correlation 

Distinct81
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0089991
Minimum-1
Maximum2.04
Zeros0
Zeros (%)0.0%
Negative23190
Negative (%)19.5%
Memory size929.2 KiB
2024-11-29T03:50:22.109568image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q11
median1.3466667
Q31.76
95-th percentile2.0266667
Maximum2.04
Range3.04
Interquartile range (IQR)0.76

Descriptive statistics

Standard deviation1.0441538
Coefficient of variation (CV)1.0348412
Kurtosis-0.13423794
Mean1.0089991
Median Absolute Deviation (MAD)0.34666667
Skewness-1.163099
Sum119987.14
Variance1.0902571
MonotonicityNot monotonic
2024-11-29T03:50:22.223083image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1 23190
19.5%
1 13526
 
11.4%
2 5827
 
4.9%
1.04 5735
 
4.8%
2.026666667 5345
 
4.5%
1.573333333 4802
 
4.0%
1.173333333 3846
 
3.2%
1.346666667 3556
 
3.0%
1.706666667 3385
 
2.8%
1.013333333 3049
 
2.6%
Other values (71) 46656
39.2%
ValueCountFrequency (%)
-1 23190
19.5%
1 13526
11.4%
1.013333333 3049
 
2.6%
1.026666667 13
 
< 0.1%
1.04 5735
 
4.8%
1.053333333 250
 
0.2%
1.066666667 525
 
0.4%
1.07445857 222
 
0.2%
1.08 631
 
0.5%
1.083333333 104
 
0.1%
ValueCountFrequency (%)
2.04 678
 
0.6%
2.032511106 538
 
0.5%
2.026666667 5345
4.5%
2.013333333 1559
 
1.3%
2 5827
4.9%
1.997189947 268
 
0.2%
1.986666667 2756
2.3%
1.973333333 1006
 
0.8%
1.96 161
 
0.1%
1.946666667 1577
 
1.3%

population
Real number (ℝ)

High correlation 

Distinct426
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4836802
Minimum1
Maximum12.767484
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size929.2 KiB
2024-11-29T03:50:22.340441image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.0071334
Q11.0382275
median1.2388702
Q31.5066849
95-th percentile2.1686553
Maximum12.767484
Range11.767484
Interquartile range (IQR)0.46845747

Descriptive statistics

Standard deviation1.3365421
Coefficient of variation (CV)0.90082896
Kurtosis59.920797
Mean1.4836802
Median Absolute Deviation (MAD)0.2063996
Skewness7.5440015
Sum176434.8
Variance1.7863448
MonotonicityNot monotonic
2024-11-29T03:50:22.467488image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.353334517 1006
 
0.8%
1.355258721 961
 
0.8%
1.350525336 957
 
0.8%
2.004527671 881
 
0.7%
1.03414855 871
 
0.7%
2 863
 
0.7%
1.515985518 855
 
0.7%
1.649419475 851
 
0.7%
1.651501074 851
 
0.7%
1.033642284 850
 
0.7%
Other values (416) 109971
92.5%
ValueCountFrequency (%)
1 4
 
< 0.1%
1.00052611 21
 
< 0.1%
1.001143996 7
 
< 0.1%
1.00115855 56
 
< 0.1%
1.001419962 103
 
0.1%
1.001780848 224
0.2%
1.001800063 147
 
0.1%
1.002195008 388
0.3%
1.002447205 180
0.2%
1.002659153 446
0.4%
ValueCountFrequency (%)
12.76748385 374
0.3%
12.75950595 273
0.2%
12.73412598 186
0.2%
12.69443396 129
 
0.1%
12.63819355 80
 
0.1%
12.61574117 104
 
0.1%
12.56876735 48
 
< 0.1%
12.52321419 72
 
0.1%
12.50109656 48
 
< 0.1%
12.43051548 13
 
< 0.1%

prev_perc
Real number (ℝ)

High correlation 

Distinct3602
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.057682063
Minimum5.9541615 × 10-7
Maximum0.66680351
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size929.2 KiB
2024-11-29T03:50:22.586014image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum5.9541615 × 10-7
5-th percentile0.00011181244
Q10.0024639518
median0.01879598
Q30.085566763
95-th percentile0.21944991
Maximum0.66680351
Range0.66680292
Interquartile range (IQR)0.083102812

Descriptive statistics

Standard deviation0.091632685
Coefficient of variation (CV)1.588582
Kurtosis11.834285
Mean0.057682063
Median Absolute Deviation (MAD)0.018557644
Skewness3.0975691
Sum6859.3778
Variance0.008396549
MonotonicityNot monotonic
2024-11-29T03:50:22.711536image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.09559488354 801
 
0.7%
0.09683559853 796
 
0.7%
0.09432083564 727
 
0.6%
4.676067381 × 10-5660
 
0.6%
0.09809551509 636
 
0.5%
0.0929705782 619
 
0.5%
0.00593172072 613
 
0.5%
0.08484053454 609
 
0.5%
0.08457552731 553
 
0.5%
0.08507706745 551
 
0.5%
Other values (3592) 112352
94.5%
ValueCountFrequency (%)
5.954161519 × 10-712
 
< 0.1%
6.021349343 × 10-712
 
< 0.1%
6.048398308 × 10-712
 
< 0.1%
6.12531721 × 10-79
 
< 0.1%
3.141321775 × 10-51
 
< 0.1%
3.205414826 × 10-512
 
< 0.1%
3.283758691 × 10-512
 
< 0.1%
3.520129062 × 10-524
< 0.1%
3.526905456 × 10-524
< 0.1%
3.528696692 × 10-534
< 0.1%
ValueCountFrequency (%)
0.6668035127 9
 
< 0.1%
0.6666800542 3
 
< 0.1%
0.6652176577 12
< 0.1%
0.6638663455 12
< 0.1%
0.6630121911 12
< 0.1%
0.6621942684 12
< 0.1%
0.6036428326 15
< 0.1%
0.6000781942 24
< 0.1%
0.5962034647 24
< 0.1%
0.5931450793 24
< 0.1%

price_month
Real number (ℝ)

High correlation 

Distinct3602
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.91244169
Minimum-1
Maximum39.343041
Zeros0
Zeros (%)0.0%
Negative24152
Negative (%)20.3%
Memory size929.2 KiB
2024-11-29T03:50:22.839058image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q11.0028542
median1.015832
Q31.3165946
95-th percentile2.2257707
Maximum39.343041
Range40.343041
Interquartile range (IQR)0.31374036

Descriptive statistics

Standard deviation1.4145108
Coefficient of variation (CV)1.5502479
Kurtosis225.91224
Mean0.91244169
Median Absolute Deviation (MAD)0.20162333
Skewness8.6879257
Sum108504.83
Variance2.0008409
MonotonicityNot monotonic
2024-11-29T03:50:22.971088image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1 24152
 
20.3%
1.004638095 335
 
0.3%
1.006020604 323
 
0.3%
1.004771886 306
 
0.3%
1.005262454 285
 
0.2%
1.006555769 278
 
0.2%
1.004370512 275
 
0.2%
1.00561923 262
 
0.2%
1.005663827 256
 
0.2%
1.003121795 251
 
0.2%
Other values (3592) 92194
77.5%
ValueCountFrequency (%)
-1 24152
20.3%
1 30
 
< 0.1%
1.00010406 36
 
< 0.1%
1.000108307 7
 
< 0.1%
1.000121049 12
 
< 0.1%
1.000133791 20
 
< 0.1%
1.000152904 41
 
< 0.1%
1.00015609 4
 
< 0.1%
1.000170955 24
 
< 0.1%
1.000178388 9
 
< 0.1%
ValueCountFrequency (%)
39.34304063 49
< 0.1%
10.1026829 9
 
< 0.1%
9.233829847 60
0.1%
9.073092097 13
 
< 0.1%
8.946663888 16
 
< 0.1%
8.704930949 18
 
< 0.1%
8.50771777 19
 
< 0.1%
8.179681577 6
 
< 0.1%
8.094738344 12
 
< 0.1%
8.048833787 24
 
< 0.1%

price_unit
Real number (ℝ)

High correlation  Skewed 

Distinct114563
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4633047
Minimum-1
Maximum535.92652
Zeros0
Zeros (%)0.0%
Negative239
Negative (%)0.2%
Memory size929.2 KiB
2024-11-29T03:50:23.093606image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile1.0037258
Q11.0118184
median1.0854146
Q31.4204449
95-th percentile2.4369209
Maximum535.92652
Range536.92652
Interquartile range (IQR)0.40862642

Descriptive statistics

Standard deviation5.4641702
Coefficient of variation (CV)3.7341302
Kurtosis8476.3857
Mean1.4633047
Median Absolute Deviation (MAD)0.081198512
Skewness87.52047
Sum174011.8
Variance29.857156
MonotonicityNot monotonic
2024-11-29T03:50:23.214127image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1 239
 
0.2%
1 196
 
0.2%
1.002194421 60
 
0.1%
1.006073823 54
 
< 0.1%
1.006870241 53
 
< 0.1%
1.130560009 53
 
< 0.1%
1.584741207 53
 
< 0.1%
1.009217574 52
 
< 0.1%
1.069418063 47
 
< 0.1%
1.009079098 47
 
< 0.1%
Other values (114553) 118063
99.3%
ValueCountFrequency (%)
-1 239
0.2%
1 196
0.2%
1.000000043 2
 
< 0.1%
1.000027661 1
 
< 0.1%
1.000082771 1
 
< 0.1%
1.000138396 1
 
< 0.1%
1.000143254 1
 
< 0.1%
1.000150809 1
 
< 0.1%
1.000152479 1
 
< 0.1%
1.000158363 1
 
< 0.1%
ValueCountFrequency (%)
535.9265168 11
< 0.1%
83.70638466 26
< 0.1%
27.83922423 1
 
< 0.1%
27.80384683 1
 
< 0.1%
27.68080382 1
 
< 0.1%
27.48212787 1
 
< 0.1%
26.89041286 1
 
< 0.1%
26.71832755 1
 
< 0.1%
26.69198797 1
 
< 0.1%
26.6798975 1
 
< 0.1%

public_perc_che
Real number (ℝ)

High correlation 

Distinct92
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7620059
Minimum-1
Maximum2.0447761
Zeros0
Zeros (%)0.0%
Negative741
Negative (%)0.6%
Memory size929.2 KiB
2024-11-29T03:50:23.328643image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile1.238806
Q11.6716418
median1.8358209
Q31.9253731
95-th percentile2.0149254
Maximum2.0447761
Range3.0447761
Interquartile range (IQR)0.25373134

Descriptive statistics

Standard deviation0.30340097
Coefficient of variation (CV)0.17219067
Kurtosis40.601716
Mean1.7620059
Median Absolute Deviation (MAD)0.10447761
Skewness-5.0193021
Sum209532.46
Variance0.092052151
MonotonicityNot monotonic
2024-11-29T03:50:23.444917image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 9724
 
8.2%
1.805970149 6600
 
5.6%
1.940298507 5430
 
4.6%
1.895522388 5341
 
4.5%
1.791044776 5174
 
4.4%
1.925373134 5104
 
4.3%
1.850746269 5073
 
4.3%
1.865671642 4811
 
4.0%
1.910447761 4600
 
3.9%
2.014925373 4335
 
3.6%
Other values (82) 62725
52.7%
ValueCountFrequency (%)
-1 741
0.6%
1 12
 
< 0.1%
1.014925373 12
 
< 0.1%
1.02104729 12
 
< 0.1%
1.029850746 10
 
< 0.1%
1.044776119 26
 
< 0.1%
1.059701493 42
 
< 0.1%
1.089552239 1
 
< 0.1%
1.104477612 12
 
< 0.1%
1.134328358 12
 
< 0.1%
ValueCountFrequency (%)
2.044776119 840
 
0.7%
2.029850746 3070
 
2.6%
2.014925373 4335
3.6%
2 9724
8.2%
1.985846555 600
 
0.5%
1.985074627 100
 
0.1%
1.970149254 552
 
0.5%
1.955223881 1604
 
1.3%
1.940298507 5430
4.6%
1.940104356 268
 
0.2%

therapeutic_area
Categorical

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size929.2 KiB
THER_AREA_96D7
45858 
THER_AREA_66C5
22024 
THER_AREA_980E
20298 
THER_AREA_6CEE
11871 
THER_AREA_644A
7579 
Other values (7)
11287 

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters1664838
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTHER_AREA_980E
2nd rowTHER_AREA_96D7
3rd rowTHER_AREA_96D7
4th rowTHER_AREA_6CEE
5th rowTHER_AREA_6CEE

Common Values

ValueCountFrequency (%)
THER_AREA_96D7 45858
38.6%
THER_AREA_66C5 22024
18.5%
THER_AREA_980E 20298
17.1%
THER_AREA_6CEE 11871
 
10.0%
THER_AREA_644A 7579
 
6.4%
THER_AREA_CD59 4578
 
3.8%
THER_AREA_032C 2011
 
1.7%
THER_AREA_4BA5 1628
 
1.4%
THER_AREA_8E53 1523
 
1.3%
THER_AREA_051D 846
 
0.7%
Other values (2) 701
 
0.6%

Length

2024-11-29T03:50:23.555444image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ther_area_96d7 45858
38.6%
ther_area_66c5 22024
18.5%
ther_area_980e 20298
17.1%
ther_area_6cee 11871
 
10.0%
ther_area_644a 7579
 
6.4%
ther_area_cd59 4578
 
3.8%
ther_area_032c 2011
 
1.7%
ther_area_4ba5 1628
 
1.4%
ther_area_8e53 1523
 
1.3%
ther_area_051d 846
 
0.7%
Other values (2) 701
 
0.6%

Most occurring characters

ValueCountFrequency (%)
E 283509
17.0%
A 247041
14.8%
R 237834
14.3%
_ 237834
14.3%
T 118917
7.1%
H 118917
7.1%
6 109945
 
6.6%
9 70734
 
4.2%
D 51394
 
3.1%
7 45858
 
2.8%
Other values (10) 142855
8.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1664838
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 283509
17.0%
A 247041
14.8%
R 237834
14.3%
_ 237834
14.3%
T 118917
7.1%
H 118917
7.1%
6 109945
 
6.6%
9 70734
 
4.2%
D 51394
 
3.1%
7 45858
 
2.8%
Other values (10) 142855
8.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1664838
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 283509
17.0%
A 247041
14.8%
R 237834
14.3%
_ 237834
14.3%
T 118917
7.1%
H 118917
7.1%
6 109945
 
6.6%
9 70734
 
4.2%
D 51394
 
3.1%
7 45858
 
2.8%
Other values (10) 142855
8.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1664838
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 283509
17.0%
A 247041
14.8%
R 237834
14.3%
_ 237834
14.3%
T 118917
7.1%
H 118917
7.1%
6 109945
 
6.6%
9 70734
 
4.2%
D 51394
 
3.1%
7 45858
 
2.8%
Other values (10) 142855
8.6%

target
Real number (ℝ)

Distinct112515
Distinct (%)94.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.420171
Minimum1
Maximum28.576068
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size929.2 KiB
2024-11-29T03:50:23.654963image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.0025139
Q11.0222512
median1.0890622
Q31.3092273
95-th percentile2.8037516
Maximum28.576068
Range27.576068
Interquartile range (IQR)0.28697616

Descriptive statistics

Standard deviation1.1833305
Coefficient of variation (CV)0.83323098
Kurtosis97.597582
Mean1.420171
Median Absolute Deviation (MAD)0.080570636
Skewness8.1239614
Sum168882.48
Variance1.4002711
MonotonicityNot monotonic
2024-11-29T03:50:23.766990image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 335
 
0.3%
1.422550727 6
 
< 0.1%
1.006525941 5
 
< 0.1%
1.000006181 5
 
< 0.1%
1.002756112 5
 
< 0.1%
1.003030932 5
 
< 0.1%
1.003022759 5
 
< 0.1%
1.003849005 5
 
< 0.1%
1.000473016 5
 
< 0.1%
1.001559779 5
 
< 0.1%
Other values (112505) 118536
99.7%
ValueCountFrequency (%)
1 335
0.3%
1.000000008 1
 
< 0.1%
1.000001788 1
 
< 0.1%
1.000002796 1
 
< 0.1%
1.000004597 1
 
< 0.1%
1.000005248 1
 
< 0.1%
1.000005676 1
 
< 0.1%
1.000006181 5
 
< 0.1%
1.000006896 1
 
< 0.1%
1.000007662 1
 
< 0.1%
ValueCountFrequency (%)
28.57606791 1
< 0.1%
27.52764155 1
< 0.1%
27.29199202 1
< 0.1%
27.08169075 1
< 0.1%
26.8762259 1
< 0.1%
26.84520067 1
< 0.1%
26.54169678 1
< 0.1%
26.07133788 1
< 0.1%
25.77912202 1
< 0.1%
24.75160313 1
< 0.1%

Interactions

2024-11-29T03:50:16.334709image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T03:50:09.772027image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T03:50:10.595026image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T03:50:11.376689image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T03:50:12.256721image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T03:50:13.025349image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T03:50:13.815403image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T03:50:14.619142image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T03:50:15.438290image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T03:50:16.423226image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T03:50:09.869542image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T03:50:10.684538image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T03:50:11.466094image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T03:50:12.343872image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T03:50:13.117869image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T03:50:13.912519image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T03:50:14.709656image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T03:50:15.523809image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T03:50:16.505741image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T03:50:09.954549image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T03:50:10.772574image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T03:50:11.552104image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T03:50:12.435103image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T03:50:13.204384image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T03:50:13.999039image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T03:50:14.793174image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T03:50:15.612324image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T03:50:16.590266image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T03:50:10.041056image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T03:50:10.861092image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T03:50:11.636615image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T03:50:12.524206image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T03:50:13.290907image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T03:50:14.089555image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T03:50:14.881690image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T03:50:15.803356image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T03:50:16.690782image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T03:50:10.125574image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T03:50:10.945096image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T03:50:11.818973image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T03:50:12.602720image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T03:50:13.375422image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T03:50:14.177068image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T03:50:14.972220image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T03:50:15.889870image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T03:50:16.780298image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T03:50:10.224102image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T03:50:11.034615image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T03:50:11.908494image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T03:50:12.693241image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T03:50:13.464530image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T03:50:14.268585image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T03:50:15.090737image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T03:50:15.981640image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T03:50:16.867809image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T03:50:10.327289image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T03:50:11.127142image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T03:50:12.002674image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T03:50:12.781761image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T03:50:13.556537image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T03:50:14.358590image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T03:50:15.194251image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T03:50:16.078655image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T03:50:16.948816image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T03:50:10.414392image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T03:50:11.207659image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T03:50:12.087199image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T03:50:12.862271image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T03:50:13.641048image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T03:50:14.444101image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T03:50:15.273769image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T03:50:16.162683image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T03:50:17.032879image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T03:50:10.501504image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T03:50:11.294180image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T03:50:12.172713image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T03:50:12.946271image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T03:50:13.729562image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T03:50:14.534616image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T03:50:15.356776image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T03:50:16.251201image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-11-29T03:50:23.973028image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
che_pc_usdche_perc_gdpcountryinsurance_perc_chepopulationprev_percprice_monthprice_unitpublic_perc_chetargettherapeutic_area
che_pc_usd1.0000.7760.921-0.169-0.4300.0420.2630.2230.3980.1770.086
che_perc_gdp0.7761.0000.8380.033-0.0420.0020.2160.1820.2850.3350.105
country0.9210.8381.0000.9621.0000.2300.1190.0590.9730.1070.148
insurance_perc_che-0.1690.0330.9621.0000.1910.022-0.0290.018-0.0190.0970.078
population-0.430-0.0421.0000.1911.000-0.081-0.006-0.093-0.1580.3600.070
prev_perc0.0420.0020.2300.022-0.0811.000-0.423-0.619-0.003-0.1090.373
price_month0.2630.2160.119-0.029-0.006-0.4231.0000.5570.2620.3400.108
price_unit0.2230.1820.0590.018-0.093-0.6190.5571.0000.1460.2650.022
public_perc_che0.3980.2850.973-0.019-0.158-0.0030.2620.1461.0000.1480.084
target0.1770.3350.1070.0970.360-0.1090.3400.2650.1481.0000.049
therapeutic_area0.0860.1050.1480.0780.0700.3730.1080.0220.0840.0491.000

Missing values

2024-11-29T03:50:17.178903image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-29T03:50:17.496457image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

brandche_pc_usdche_perc_gdpcluster_nlcorporationcountrylaunch_datedatedrug_idind_launch_dateindicationinsurance_perc_chepopulationprev_percprice_monthprice_unitpublic_perc_chetherapeutic_areatarget
0BRAND_354E1.2091141.665879BRAND_354E_COUNTRY_88A3CORP_D524COUNTRY_88A32014-06-012014-06-01DRUG_ID_8795-1['IND_C3B6']1.8933331.0080390.0283671.0064441.0137841.835821THER_AREA_980E1.000784
1BRAND_626D-1.000000-1.000000BRAND_626D_COUNTRY_8B47CORP_01C7COUNTRY_8B472014-06-012014-06-01DRUG_ID_E66E2014-09-01 00:00:00['IND_1590', 'IND_ECAC']-1.0000001.0235620.000047-1.0000001.626677-1.000000THER_AREA_96D71.000000
2BRAND_45D91.2091141.665879BRAND_45D9_COUNTRY_88A3CORP_39F7COUNTRY_88A32014-06-012014-06-01DRUG_ID_F272-1['IND_B2EF']1.8933331.0080390.001502-1.0000003.1448741.835821THER_AREA_96D71.002258
3BRAND_D7241.8512802.051770BRAND_D724_COUNTRY_445DCORP_711ACOUNTRY_445D2014-06-012014-06-01DRUG_ID_1D4E-1['IND_BAFB']1.0000001.2531860.001304-1.0000001.2134461.805970THER_AREA_6CEE1.068761
4BRAND_48871.7911992.059130BRAND_4887_COUNTRY_D8B0CORP_443DCOUNTRY_D8B02014-06-012014-06-01DRUG_ID_AA88-1['IND_3F31']2.0133331.6393520.0544671.0185891.0087081.880597THER_AREA_6CEE1.036312
5BRAND_6E6E1.1323351.514478BRAND_6E6E_COUNTRY_9488CORP_711ACOUNTRY_94882014-06-012014-06-01DRUG_ID_0383-1['IND_BAFB']1.8000001.2790480.002884-1.0000001.1113911.791045THER_AREA_6CEE1.000821
6BRAND_03C21.8122661.953901BRAND_03C2_COUNTRY_9891CORP_B65DCOUNTRY_98912014-06-012014-06-01DRUG_ID_E0F1-1['IND_01DA']1.5733331.0333530.3495081.0004011.0011021.820896THER_AREA_644A1.000280
7BRAND_626D1.237984-1.000000BRAND_626D_COUNTRY_5180CORP_01C7COUNTRY_51802014-06-012014-06-01DRUG_ID_E66E-1['IND_1590', 'IND_ECAC', 'IND_D925']1.9333331.0507960.000051-1.0000002.1396541.985075THER_AREA_96D71.002721
8BRAND_F05A2.4428841.892659BRAND_F05A_COUNTRY_3AD0CORP_7E54COUNTRY_3AD02014-06-012014-06-01DRUG_ID_47B7-1['IND_4000', 'IND_3A0D']1.5733331.0301150.0954411.0036791.0257221.238806THER_AREA_66C51.030898
9BRAND_CCAA1.6690071.764281BRAND_CCAA_COUNTRY_89F9CORP_8F4FCOUNTRY_89F92014-06-012014-06-01DRUG_ID_07FE-1['IND_F338', 'IND_36A0']-1.0000001.4954850.0124991.6586091.7904051.940299THER_AREA_96D71.111504
brandche_pc_usdche_perc_gdpcluster_nlcorporationcountrylaunch_datedatedrug_idind_launch_dateindicationinsurance_perc_chepopulationprev_percprice_monthprice_unitpublic_perc_chetherapeutic_areatarget
118907BRAND_F148-1.000000-1.000000BRAND_F148_COUNTRY_8B47CORP_01C7COUNTRY_8B472019-03-012022-12-01DRUG_ID_BE952020-03-01 00:00:00['IND_F338']-1.0000001.0251990.003552-1.0000001.746809-1.000000THER_AREA_96D71.295619
118908BRAND_BDB61.1834431.529843BRAND_BDB6_COUNTRY_FA79CORP_A682COUNTRY_FA792021-07-012022-12-01DRUG_ID_8AB0-1['IND_DA0B', 'IND_BD8B']1.7858061.0429120.0333051.1264771.4032951.794286THER_AREA_032C1.035240
118909BRAND_76D61.340020-1.000000BRAND_76D6_COUNTRY_5180CORP_A682COUNTRY_51802018-05-012022-12-01DRUG_ID_45AB-1['IND_8EA5']1.9200001.0519390.0188082.3927981.4519532.044776THER_AREA_96D71.632343
118910BRAND_50D81.2708271.684065BRAND_50D8_COUNTRY_6B71CORP_B3B2COUNTRY_6B712018-01-012022-12-01DRUG_ID_D235-1['IND_7671']1.4400001.0496280.020591-1.0000001.0010841.552239THER_AREA_CD591.006651
118911BRAND_280C1.4628281.918488BRAND_280C_COUNTRY_907ECORP_7883COUNTRY_907E2020-10-012022-12-01DRUG_ID_EC562020-02-01 00:00:00['IND_3A0D']1.0400001.3562780.0929421.0050841.0083371.843706THER_AREA_66C51.005538
118912BRAND_20582.0744382.058055BRAND_2058_COUNTRY_C8F4CORP_3C9ACOUNTRY_C8F42020-11-012022-12-01DRUG_ID_74A6-1['IND_A496']-1.0000001.0498080.0952331.0540071.1003362.029851THER_AREA_6CEE1.203657
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